Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization
About
Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Sound Source Localization | Flickr SoundNet (test) | CIoU84.8 | 28 | |
| Audio referred image grounding | PascalSound (test) | cIoU55.48 | 10 | |
| Audio referred image grounding | AVSBench (test) | cIoU67.49 | 10 | |
| Audio referred image grounding | VGG-SS (test) | cIoU39.4 | 10 |